Biostat 203B Homework 2

Due Feb 8 Feb 11, 2026 @ 11:59PM

Display machine information for reproducibility:

sessionInfo()

R version 4.4.2 (2024-10-31)
Platform: aarch64-apple-darwin20
Running under: macOS Sequoia 15.2

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRblas.0.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/Los_Angeles
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] htmlwidgets_1.6.4 compiler_4.4.2    fastmap_1.2.0     cli_3.6.5        
 [5] tools_4.4.2       htmltools_0.5.8.1 rstudioapi_0.17.1 yaml_2.3.10      
 [9] rmarkdown_2.29    knitr_1.49        jsonlite_2.0.0    xfun_0.50        
[13] digest_0.6.37     rlang_1.1.7       evaluate_1.0.5   

Load necessary libraries (you can add more as needed).

library(arrow)
library(data.table)
library(duckdb)
library(memuse)
#library(pryr) didn't work for my version of R
library(lobstr)
library(R.utils)
library(tidyverse)

Display memory information of your computer

memuse::Sys.meminfo()

Totalram:   16.000 GiB 
Freeram:   168.562 MiB 

In this exercise, we explore various tools for ingesting the MIMIC-IV data introduced in homework 1.

Display the contents of MIMIC hosp and icu data folders:

ls -l ~/mimic/hosp/

total 12451224
-rw-r--r--  1 charlottelee  staff    94156458 Jun 24  2024 admissions.csv
-rw-r--r--@ 1 charlottelee  staff      427554 Apr 12  2024 d_hcpcs.csv.gz
-rw-r--r--@ 1 charlottelee  staff      876360 Apr 12  2024 d_icd_diagnoses.csv.gz
-rw-r--r--@ 1 charlottelee  staff      589186 Apr 12  2024 d_icd_procedures.csv.gz
-rw-r--r--@ 1 charlottelee  staff       13169 Oct  3  2024 d_labitems.csv.gz
-rw-r--r--@ 1 charlottelee  staff    33564802 Oct  3  2024 diagnoses_icd.csv.gz
-rw-r--r--@ 1 charlottelee  staff     9743908 Oct  3  2024 drgcodes.csv.gz
-rw-r--r--@ 1 charlottelee  staff   811305629 Apr 12  2024 emar.csv.gz
-rw-r--r--@ 1 charlottelee  staff   748158322 Apr 12  2024 emar_detail.csv.gz
-rw-r--r--@ 1 charlottelee  staff     2162335 Apr 12  2024 hcpcsevents.csv.gz
-rw-r--r--@ 1 charlottelee  staff  2592909134 Oct  3  2024 labevents.csv.gz
-rw-r--r--@ 1 charlottelee  staff   117644075 Oct  3  2024 microbiologyevents.csv.gz
-rw-r--r--@ 1 charlottelee  staff    44069351 Oct  3  2024 omr.csv.gz
-rw-r--r--@ 1 charlottelee  staff     2835586 Apr 12  2024 patients.csv.gz
-rw-r--r--@ 1 charlottelee  staff   525708076 Apr 12  2024 pharmacy.csv.gz
-rw-r--r--@ 1 charlottelee  staff   666594177 Apr 12  2024 poe.csv.gz
-rw-r--r--@ 1 charlottelee  staff    55267894 Apr 12  2024 poe_detail.csv.gz
-rw-r--r--@ 1 charlottelee  staff   606298611 Apr 12  2024 prescriptions.csv.gz
-rw-r--r--@ 1 charlottelee  staff     7777324 Apr 12  2024 procedures_icd.csv.gz
-rw-r--r--@ 1 charlottelee  staff      127330 Apr 12  2024 provider.csv.gz
-rw-r--r--@ 1 charlottelee  staff     8569241 Apr 12  2024 services.csv.gz
-rw-r--r--@ 1 charlottelee  staff    46185771 Oct  3  2024 transfers.csv.gz

ls -l ~/mimic/icu/

total 8506784
-rw-r--r--@ 1 charlottelee  staff       41566 Apr 12  2024 caregiver.csv.gz
-rw-r--r--@ 1 charlottelee  staff  3502392765 Apr 12  2024 chartevents.csv.gz
-rw-r--r--@ 1 charlottelee  staff       58741 Apr 12  2024 d_items.csv.gz
-rw-r--r--@ 1 charlottelee  staff    63481196 Apr 12  2024 datetimeevents.csv.gz
-rw-r--r--@ 1 charlottelee  staff     3342355 Oct  3  2024 icustays.csv.gz
-rw-r--r--@ 1 charlottelee  staff   311642048 Apr 12  2024 ingredientevents.csv.gz
-rw-r--r--@ 1 charlottelee  staff   401088206 Apr 12  2024 inputevents.csv.gz
-rw-r--r--@ 1 charlottelee  staff    49307639 Apr 12  2024 outputevents.csv.gz
-rw-r--r--@ 1 charlottelee  staff    24096834 Apr 12  2024 procedureevents.csv.gz

Q1. read.csv (base R) vs read_csv (tidyverse) vs fread (data.table)

Q1.1 Speed, memory, and data types

There are quite a few utilities in R for reading plain text data files. Let us test the speed of reading a moderate sized compressed csv file, admissions.csv.gz, by three functions: read.csv in base R, read_csv in tidyverse, and fread in the data.table package.

Which function is fastest? Is there difference in the (default) parsed data types? How much memory does each resultant dataframe or tibble use? (Hint: system.time measures run times; pryr::object_size measures memory usage; all these readers can take gz file as input without explicit decompression.)

• Elapsed time: total wall-clock time

  • The fastest function was the fread() function with a speed of 0.302s compared to the 0.510s (read_csv) and 4.394s (read.csv).

• Default parsed types:

  • Yes, there is a differnce in parsed type because read.csv() makes a data.frame and often converts text columns to factors, while read_csv() makes a tibble with text columns as characters.

• The amount of memory each resultant uses:

-   read.csv: 200.10 MB

-   read_csv: 70.02 MB

-   fread: 63.47 MB

• Overall: fread: fread() seems to make a data.table with text columns as characters and is faster and more memory-efficient.

text_file <-"/Users/charlottelee/Downloads/mimic-iv-3.1/hosp/admissions.csv.gz"

# Comparing Function Speed
#read.csv
system.time(read.csv(text_file))

   user  system elapsed 
  5.748   0.139   5.923 

#read_csv
system.time(read_csv((text_file), show_col_types = FALSE))

   user  system elapsed 
  1.011   0.127   0.591 

#fread
system.time(fread((text_file)))

   user  system elapsed 
  0.586   0.177   0.312 

file <-"/Users/charlottelee/Downloads/mimic-iv-3.1/hosp/admissions.csv.gz"
# Comparing class types
time_base <- system.time({
  admissions_base <- read.csv(file)
})

# 2. Tidyverse / readr
time_tidy <- system.time({
  admissions_tidy <- read_csv(file, show_col_types = FALSE)
})

# 3. data.table / fread
time_dt <- system.time({
  admissions_dt <- fread(file)
})

  # read.csv → data.frame
class(admissions_base)   

[1] "data.frame"

  # read_csv → tibble
class(admissions_tidy)  

[1] "spec_tbl_df" "tbl_df"      "tbl"         "data.frame" 

  # fread → data.table
class(admissions_dt)     

[1] "data.table" "data.frame"

# Comparing Sizes
#read.csv
lobstr::obj_size(admissions_base)

200.10 MB

#read_csv
lobstr::obj_size(admissions_tidy)

70.02 MB

#fread
lobstr::obj_size(admissions_dt)

63.47 MB

Q1.2 User-supplied data types

Re-ingest admissions.csv.gz by indicating appropriate column data types in read_csv. Does the run time change? How much memory does the result tibble use? (Hint: col_types argument in read_csv; result tibble should be less than 50MB.)

• The run time changed from 0.510s previously to around 0.533s which is evidently slightly slower than before.

• The resulting tibble is 70.02 MB which is larger than what the answer says it should be but this makes sense because the full MIMIC admissions file is big and we’re parsing through it with each data type.

file <-"/Users/charlottelee/Downloads/mimic-iv-3.1/hosp/admissions.csv.gz"

# Preview first 100 rows to infer types
#admissions_preview <- read_csv(file, n_max = 100)
#glimpse(admissions_preview)

# Define column types based on your glimpse
col_types <- cols(
  subject_id           = col_double(),
  hadm_id              = col_double(),
  admittime            = col_datetime(),
  dischtime            = col_datetime(),
  deathtime            = col_datetime(),
  admission_type       = col_character(),
  admit_provider_id    = col_character(),
  admission_location   = col_character(),
  discharge_location   = col_character(),
  insurance            = col_character(),
  language             = col_character(),
  marital_status       = col_character(),
  race                 = col_character(),
  edregtime            = col_datetime(),
  edouttime            = col_datetime(),
  hospital_expire_flag = col_double()
)

# Re-ingest with explicit types and measure runtime
time_typed <- system.time({
  admissions_typed <- read_csv(file, col_types = col_types)
})

time_typed

   user  system elapsed 
  0.974   0.117   0.548 

#Check memory usage
lobstr::obj_size(admissions_typed)

70.02 MB

Q2. Ingest big data files

Let us focus on a bigger file, labevents.csv.gz, which is about 130x bigger than admissions.csv.gz.

ls -l ~/mimic/hosp/labevents.csv.gz

-rw-r--r--@ 1 charlottelee  staff  2592909134 Oct  3  2024 /Users/charlottelee/mimic/hosp/labevents.csv.gz

Display the first 10 lines of this file.

zcat < ~/mimic/hosp/labevents.csv.gz | head -10

Q2.1 Ingest labevents.csv.gz by read_csv

Try to ingest labevents.csv.gz using read_csv. What happens? If it takes more than 3 minutes on your computer, then abort the program and report your findings.

• Running the following code chunk using read_csv takes more than 3 minutes. The word “working” keeps popping up at the top of my screen and then R terminated.
• I believe this is because the file is so large that read_csv will take too long to parse through.

file <-"/Users/charlottelee/Downloads/mimic-iv-3.1/hosp/labevents.csv.gz" 
time_labevents <- system.time({
  labevents <- read_csv(file)
})

Q2.2 Ingest selected columns of labevents.csv.gz by read_csv

Try to ingest only columns subject_id, itemid, charttime, and valuenum in labevents.csv.gz using read_csv. Does this solve the ingestion issue? (Hint: col_select argument in read_csv.)

• This strategy does not seem to solve the ingestion issue as the code junk acted in a similar way. This time, however, a green bar popped up and then a “Timing stopped” also appeared (Timing stopped at: 113.2 157 338.9).

file <-"/Users/charlottelee/Downloads/mimic-iv-3.1/hosp/labevents.csv.gz" 

time_labevents <- system.time({
  labevents_subset <- read_csv(
    file,
    col_select = c("subject_id", "itemid", "charttime", "valuenum")
  )
})

# Check runtime
time_labevents

# Check memory usage
lobstr::obj_size(labevents_subset)

# Check the first few rows
head(labevents_subset)

Q2.3 Ingest a subset of labevents.csv.gz

Our first strategy to handle this big data file is to make a subset of the labevents data. Read the MIMIC documentation for the content in data file labevents.csv.

In later exercises, we will only be interested in the following lab items: albumin (50862), creatinine (50912), potassium (50971), sodium (50983), chloride (50902), bicarbonate (50882), hematocrit (51221), white blood cell count (51301), and glucose (50931) and the following columns: subject_id, itemid, charttime, valuenum. Write a Bash command to extract these columns and rows from labevents.csv.gz and save the result to a new file labevents_filtered.csv.gz in the current working directory. (Hint: Use zcat < to pipe the output of labevents.csv.gz to awk and then to gzip to compress the output. Do not put labevents_filtered.csv.gz in Git! To save render time, you can put #| eval: false at the beginning of this code chunk. TA will change it to #| eval: true before rendering your qmd file.)

Display the first 10 lines of the new file labevents_filtered.csv.gz. How many lines are in this new file, excluding the header? How long does it take read_csv to ingest labevents_filtered.csv.gz?

Q2.4 Ingest labevents.csv by Apache Arrow

Our second strategy is to use Apache Arrow for larger-than-memory data analytics. Unfortunately Arrow does not work with gz files directly. First decompress labevents.csv.gz to labevents.csv and put it in the current working directory (do not add it in Git!). To save render time, put #| eval: false at the beginning of this code chunk. TA will change it to #| eval: true when rendering your qmd file.

Then use arrow::open_dataset to ingest labevents.csv, select columns, and filter itemid as in Q2.3. How long does the ingest+select+filter process take? Display the number of rows and the first 10 rows of the result tibble, and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Write a few sentences to explain what Apache Arrow is. Imagine you want to explain it to a layman in an elevator.

Q2.5 Compress labevents.csv to Parquet format and ingest/select/filter

Re-write the csv file labevents.csv in the binary Parquet format (Hint: arrow::write_dataset.) How large is the Parquet file(s)? How long does the ingest+select+filter process of the Parquet file(s) take? Display the number of rows and the first 10 rows of the result tibble and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Write a few sentences to explain what the Parquet format is. Imagine you want to explain it to a layman in an elevator.

Q2.6 DuckDB

Ingest the Parquet file, convert it to a DuckDB table by arrow::to_duckdb, select columns, and filter rows as in Q2.5. How long does the ingest+convert+select+filter process take? Display the number of rows and the first 10 rows of the result tibble and make sure they match those in Q2.3. (Hint: use dplyr verbs for selecting columns and filtering rows.)

Write a few sentences to explain what DuckDB is. Imagine you want to explain it to a layman in an elevator.

Q3. Ingest and filter chartevents.csv.gz

chartevents.csv.gz contains all the charted data available for a patient. During their ICU stay, the primary repository of a patient’s information is their electronic chart. The itemid variable indicates a single measurement type in the database. The value variable is the value measured for itemid. The first 10 lines of chartevents.csv.gz are

zcat < ~/mimic/icu/chartevents.csv.gz | head -10

subject_id,hadm_id,stay_id,caregiver_id,charttime,storetime,itemid,value,valuenum,valueuom,warning
10000032,29079034,39553978,18704,2180-07-23 12:36:00,2180-07-23 14:45:00,226512,39.4,39.4,kg,0
10000032,29079034,39553978,18704,2180-07-23 12:36:00,2180-07-23 14:45:00,226707,60,60,Inch,0
10000032,29079034,39553978,18704,2180-07-23 12:36:00,2180-07-23 14:45:00,226730,152,152,cm,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:18:00,220048,SR (Sinus Rhythm),,,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:18:00,224642,Oral,,,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:18:00,224650,None,,,0
10000032,29079034,39553978,18704,2180-07-23 14:00:00,2180-07-23 14:20:00,223761,98.7,98.7,°F,0
10000032,29079034,39553978,18704,2180-07-23 14:11:00,2180-07-23 14:17:00,220179,84,84,mmHg,0
10000032,29079034,39553978,18704,2180-07-23 14:11:00,2180-07-23 14:17:00,220180,48,48,mmHg,0

How many rows? 433 millions.

zcat < ~/mimic/icu/chartevents.csv.gz | tail -n +2 | wc -l

d_items.csv.gz is the dictionary for the itemid in chartevents.csv.gz.

zcat < ~/mimic/icu/d_items.csv.gz | head -10

itemid,label,abbreviation,linksto,category,unitname,param_type,lownormalvalue,highnormalvalue
220001,Problem List,Problem List,chartevents,General,,Text,,
220003,ICU Admission date,ICU Admission date,datetimeevents,ADT,,Date and time,,
220045,Heart Rate,HR,chartevents,Routine Vital Signs,bpm,Numeric,,
220046,Heart rate Alarm - High,HR Alarm - High,chartevents,Alarms,bpm,Numeric,,
220047,Heart Rate Alarm - Low,HR Alarm - Low,chartevents,Alarms,bpm,Numeric,,
220048,Heart Rhythm,Heart Rhythm,chartevents,Routine Vital Signs,,Text,,
220050,Arterial Blood Pressure systolic,ABPs,chartevents,Routine Vital Signs,mmHg,Numeric,90,140
220051,Arterial Blood Pressure diastolic,ABPd,chartevents,Routine Vital Signs,mmHg,Numeric,60,90
220052,Arterial Blood Pressure mean,ABPm,chartevents,Routine Vital Signs,mmHg,Numeric,,

In later exercises, we are interested in the vitals for ICU patients: heart rate (220045), mean non-invasive blood pressure (220181), systolic non-invasive blood pressure (220179), body temperature in Fahrenheit (223761), and respiratory rate (220210). Retrieve a subset of chartevents.csv.gz only containing these items, using the favorite method you learnt in Q2.

Document the steps and show code. Display the number of rows and the first 10 rows of the result tibble.

Q4. AI assistant

Which AI assistants (e.g., GitHub Copilot) do you use when working on this assignment? Which AI model (e.g., GPT-5 mini, GPT-5, Claude Sonnet 4.5) does the AI assistant use? How do you use them? Do you think they help improve your productivity?

Give 5 instances where AI model gave incorrect or misleading answers. You can use screenshots or copy-paste the Q&A.